###1. Merges the training and the test sets to create one data set.
The first step in my code merges the data from each set, the test set and the training set, to create one set of each. I used rbind( ) to combine the Subject_ID and Activity to the X set of data, the used cbind( ) to combine the completed training and test sets to create one big data frame.
###2. Extracts only the measurements on the mean and standard deviation for each measurement. #####To extract the data I needed, I looked at the 'features.txt' file from the orginal folder and noted which columns in the set contained either "-mean( )" or "-std( )". I selected only those columns from the entire data set and created a narrower data frame of 68 columns. (The first 2 columns were Subject_ID and Activity, and colums 3-68 where the selected columns from the 'features.txt' file).
###3. Uses descriptive activity names to name the activities in the data set #####From there, I coerced the Activity column to be of the class 'Factor', then renamed the factor levels from numeric to character to give the data activity names. For example, I renamed 1 -> "WALKING", 2 -> "WALKING_UPSTAIRS", etc. There were 6 different activities.
###4. Appropriately labels the data set with descriptive variable names. #####To label columns 3-68, I used the same feature names from the 'features.txt' list using the select( ) function from dplyr.
###5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject. #####For the hardest task, I used many functions from the reshape2 and tidyr package to tidy up the data set. First, I used the melt function to put all the feature names under one column as its own 'variable', using the "Subject_ID" and "Activity" columns as the id. The 4th column displayed the value. Next, I simplified the variable column to only list "mean" or "std", then grouped the data by the first 3 columns, and found the average of the values column. From there, I used the separate function from tidyr to separate the "mean" and "std" values to become their own columns with the values listed underneath. This left us with a tidy data set of 180 rows and 4 columns, with each column as one variable and each row an observation.